
"""simple docstring"""



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


a       :						int       		=		logging.getLogger(__name__)

a       :						str       		=		'''pytorch_model.bin'''




@dataclasses.dataclass
class 		a_    :
    a   :					str			     =					dataclasses.field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}						)
    a   :					Optional[str]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}  ,    )







@dataclasses.dataclass
class 		a_    :
    a   :					str			     =					dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}						)
    a   :					str			     =					dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}						)
    a   :					Optional[str]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'A csv or a json file containing the validation data.'}						)
    a   :					Optional[str]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'The name of the task to train on.'}  ,    )
    a   :					Optional[List[str]]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'The list of labels for the task.'}						)







@dataclasses.dataclass
class 		a_    :
    a   :					str			     =					dataclasses.field(
        metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}						)
    a   :					Optional[str]			     =					dataclasses.field(
        default='accuracy'  ,    metadata={'help': 'The evaluation metric used for the task.'}						)
    a   :					Optional[str]			     =					dataclasses.field(
        default='no'  ,    metadata={
            'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
        }  ,    )
    a   :					Optional[int]			     =					dataclasses.field(
        default=10  ,    metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}  ,    )
    a   :					Optional[float]			     =					dataclasses.field(
        default=0.0  ,    metadata={
            'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
        }  ,    )
    a   :					Optional[bool]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}  ,    )
    a   :					Optional[bool]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}  ,    )
    a   :					Optional[bool]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}  ,    )
    a   :					Optional[float]			     =					dataclasses.field(
        default=0.0  ,    metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}  ,    )
    a   :					Optional[int]			     =					dataclasses.field(
        default=100  ,    metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}  ,    )
    a   :					Optional[int]			     =					dataclasses.field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Random seed for initialization.'}  ,    )







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = datasets.concatenate_datasets([infer_input, infer_output]       ,   axis=1   )

    if args.do_filter_by_confidence:
        _UpperCAmelCase        = dataset.filter(lambda _A   : example["probability"] > args.confidence_threshold   )

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        _UpperCAmelCase        = int(eval_result * len(_A   )   )
        print(_A   )
        _UpperCAmelCase        = dataset.sort("""probability"""       ,   reverse=_A   )
        _UpperCAmelCase        = dataset.select(range(_A   )   )

    _UpperCAmelCase        = dataset.remove_columns(["""label""", """probability"""]   )
    _UpperCAmelCase        = dataset.rename_column("""prediction"""       ,   """label"""   )
    _UpperCAmelCase        = dataset.map(lambda _A   : {"label": idalabel[example["label"]]}   )
    _UpperCAmelCase        = dataset.shuffle(seed=args.seed   )

    _UpperCAmelCase        = os.path.join(_A       ,   F"""train_pseudo.{args.data_file_extension}"""   )
    if args.data_file_extension == "csv":
        dataset.to_csv(_A       ,   index=_A   )
    else:
        dataset.to_json(_A   )







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   **_A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator()
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,   datefmt="""%m/%d/%Y %H:%M:%S"""       ,   level=logging.INFO       ,   )
    logger.info(accelerator.state   )

    # Setup logging, we only want one process per machine to log things on the
    # screen. accelerator.is_local_main_process is only True for one process per
    # machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR   )

    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    _UpperCAmelCase        = STModelArguments(model_name_or_path=_A   )
    _UpperCAmelCase        = STDataArguments(train_file=_A       ,   infer_file=_A   )
    _UpperCAmelCase        = STTrainingArguments(output_dir=_A   )
    _UpperCAmelCase        = argparse.Namespace()

    for arg_class in (model_args, data_args, training_args):
        for key, value in vars(_A   ).items():
            setattr(_A       ,   _A       ,   _A   )

    for key, value in kwargs.items():
        if hasattr(_A       ,   _A   ):
            setattr(_A       ,   _A       ,   _A   )

    # Sanity checks
    _UpperCAmelCase        = {}
    _UpperCAmelCase        = None

    # You need to provide the training data and the data to predict on
    assert args.train_file is not None
    assert args.infer_file is not None
    _UpperCAmelCase        = args.train_file
    _UpperCAmelCase        = args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        _UpperCAmelCase        = args.eval_file

    for key in data_files:
        _UpperCAmelCase        = data_files[key].split("""."""   )[-1]
        assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
        if args.data_file_extension is None:
            _UpperCAmelCase        = extension
        else:
            assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

    assert (
        args.eval_metric in datasets.list_metrics()
    ), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed   )

    logger.info("""Creating the initial data directory for self-training..."""   )
    _UpperCAmelCase        = F"""{args.output_dir}/self-train_iter-{{}}""".format
    _UpperCAmelCase        = data_dir_format(0   )

    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir       ,   exist_ok=_A   )
            os.makedirs(_A       ,   exist_ok=_A   )
    accelerator.wait_for_everyone()

    _UpperCAmelCase        = None
    _UpperCAmelCase        = None
    _UpperCAmelCase        = 0
    _UpperCAmelCase        = False
    # Show the progress bar
    _UpperCAmelCase        = tqdm(range(args.max_selftrain_iterations   )       ,   disable=not accelerator.is_local_main_process   )

    # Self-train
    for iteration in range(0       ,   int(args.max_selftrain_iterations   )   ):
        _UpperCAmelCase        = data_dir_format(_A   )
        assert os.path.exists(_A   )

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        _UpperCAmelCase        = os.path.join(_A       ,   """stage-1"""   )
        _UpperCAmelCase        = {
            """accelerator""": accelerator,
            """model_name_or_path""": args.model_name_or_path,
            """cache_dir""": args.cache_dir,
            """do_train""": True,
            """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
            """do_eval""": True if args.eval_file is not None else False,
            """eval_file""": data_files["""eval"""],
            """do_predict""": True,
            """infer_file""": data_files["""infer"""],
            """task_name""": args.task_name,
            """label_list""": args.label_list,
            """output_dir""": current_output_dir,
            """eval_metric""": args.eval_metric,
            """evaluation_strategy""": args.evaluation_strategy,
            """early_stopping_patience""": args.early_stopping_patience,
            """early_stopping_threshold""": args.early_stopping_threshold,
            """seed""": args.seed,
        }
        # Add additional training arguments
        for key, value in kwargs.items():
            if key not in arguments_dict and not hasattr(_A       ,   _A   ):
                arguments_dict.update({key: value}   )

        _UpperCAmelCase        = os.path.join(_A       ,   """best-checkpoint"""       ,   _A   )
        if os.path.exists(_A   ):
            logger.info(
                """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""       ,   _A       ,   _A       ,   )
        else:
            logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""       ,   _A   )
            finetune(**_A   )
            accelerator.wait_for_everyone()
            assert os.path.exists(_A   )
            logger.info("""Self-training job completed: iteration: %d, stage: 1."""       ,   _A   )

        if iteration > 0 and args.finetune_on_labeled_data:
            # Stage 2 (optional): fine-tuning on the original labeled data
            _UpperCAmelCase        = os.path.join(_A       ,   """best-checkpoint"""   )
            _UpperCAmelCase        = os.path.join(_A       ,   """stage-2"""   )
            # Update arguments_dict
            _UpperCAmelCase        = model_path
            _UpperCAmelCase        = data_files["""train"""]
            _UpperCAmelCase        = current_output_dir

            _UpperCAmelCase        = os.path.join(_A       ,   """best-checkpoint"""       ,   _A   )
            if os.path.exists(_A   ):
                logger.info(
                    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""       ,   _A       ,   _A       ,   )
            else:
                logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""       ,   _A   )
                finetune(**_A   )
                accelerator.wait_for_everyone()
                assert os.path.exists(_A   )
                logger.info("""Self-training job completed: iteration: %d, stage: 2."""       ,   _A   )

        _UpperCAmelCase        = iteration
        _UpperCAmelCase        = data_dir_format(iteration + 1   )

        _UpperCAmelCase        = AutoConfig.from_pretrained(os.path.join(_A       ,   """best-checkpoint"""   )   )
        _UpperCAmelCase        = config.idalabel
        _UpperCAmelCase        = os.path.join(_A       ,   """eval_results_best-checkpoint.json"""   )
        _UpperCAmelCase        = os.path.join(_A       ,   """test_results_best-checkpoint.json"""   )
        assert os.path.exists(_A   )

        with open(_A       ,   """r"""   ) as f:
            _UpperCAmelCase        = float(json.load(_A   )[args.eval_metric]   )
        _UpperCAmelCase        = os.path.join(_A       ,   """infer_output_best-checkpoint.csv"""   )
        assert os.path.exists(_A   )
        # Loading the dataset from local csv or json files.
        _UpperCAmelCase        = load_dataset(args.data_file_extension       ,   data_files={"""data""": data_files["""infer"""]}   )["""data"""]
        _UpperCAmelCase        = load_dataset("""csv"""       ,   data_files={"""data""": infer_output_file}   )["""data"""]

        if accelerator.is_main_process:
            os.makedirs(_A       ,   exist_ok=_A   )
            shutil.copy(_A       ,   os.path.join(_A       ,   F"""eval_results_iter-{iteration}.json"""   )   )
            if os.path.exists(_A   ):
                shutil.copy(_A       ,   os.path.join(_A       ,   F"""test_results_iter-{iteration}.json"""   )   )
            create_pseudo_labeled_data(_A       ,   _A       ,   _A       ,   _A       ,   _A       ,   _A   )
        accelerator.wait_for_everyone()

        _UpperCAmelCase        = os.path.join(_A       ,   F"""train_pseudo.{args.data_file_extension}"""   )

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            _UpperCAmelCase        = eval_result

            if best_iteration is None:
                _UpperCAmelCase        = new_iteration
                _UpperCAmelCase        = new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    _UpperCAmelCase        = new_iteration
                    _UpperCAmelCase        = new_eval_result
                    _UpperCAmelCase        = 0
                else:
                    if new_eval_result == best_eval_result:
                        _UpperCAmelCase        = new_iteration
                        _UpperCAmelCase        = new_eval_result
                    early_stopping_patience_counter += 1

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    _UpperCAmelCase        = True

        progress_bar.update(1   )

        if should_training_stop:
            break

    if best_iteration is not None:
        # Save the best iteration
        logger.info("""Best iteration: %d"""       ,   _A   )
        logger.info("""Best evaluation result: %s = %f"""       ,   args.eval_metric       ,   _A   )
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(_A       ,   F"""eval_results_iter-{iteration}.json"""   )       ,   os.path.join(_A       ,   """eval_results_best-iteration.json"""   )       ,   )
    else:
        # Assume that the last iteration is the best
        logger.info("""Best iteration: %d"""       ,   args.max_selftrain_iterations - 1   )
        logger.info("""Best evaluation result: %s = %f"""       ,   args.eval_metric       ,   _A   )
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(_A       ,   F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""   )       ,   os.path.join(_A       ,   """eval_results_best-iteration.json"""   )       ,   )

"""simple docstring"""



from ....configuration_utils import PretrainedConfig
from ....utils import logging


a       :						Dict       		=		logging.get_logger(__name__)

# TODO: upload to AWS
a       :						Tuple       		=		{
    '''yjernite/retribert-base-uncased''': (
        '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					int			     =					'retribert'







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							Optional[Any]=3_05_22  ,       __UpperCamelCase      :							int=7_68  ,       __UpperCamelCase      :							Any=8  ,       __UpperCamelCase      :							Dict=12  ,       __UpperCamelCase      :							List[Any]=30_72  ,       __UpperCamelCase      :							List[Any]="gelu"  ,       __UpperCamelCase      :							Optional[int]=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							Union[str, Any]=5_12  ,       __UpperCamelCase      :							Dict=2  ,       __UpperCamelCase      :							Tuple=0.0_2  ,       __UpperCamelCase      :							List[str]=1e-12  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							str=1_28  ,       __UpperCamelCase      :							Union[str, Any]=0  ,       **__UpperCamelCase      :							Any  ,       )		->List[Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = share_encoders
        _UpperCAmelCase        = projection_dim